Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?

• URL: http://arxiv.org/abs/2501.17840v1
• Date: Wed, 29 Jan 2025 18:40:32 GMT
• Title: Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?
• Authors: Pouya Pezeshkpour, Estevam Hruschka,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable performance on various tasks. However, their ability to extract and internalize deeper insights from domain-specific datasets remains underexplored. This study investigates how continual pre-training can enhance LLMs' capacity for insight learning.
• Score: 4.390998479503661
• License:
• Abstract: Large Language Models (LLMs) have demonstrated remarkable performance on various tasks, yet their ability to extract and internalize deeper insights from domain-specific datasets remains underexplored. In this study, we investigate how continual pre-training can enhance LLMs' capacity for insight learning across three distinct forms: declarative, statistical, and probabilistic insights. Focusing on two critical domains: medicine and finance, we employ LoRA to train LLMs on two existing datasets. To evaluate each insight type, we create benchmarks to measure how well continual pre-training helps models go beyond surface-level knowledge. We also assess the impact of document modification on capturing insights. The results show that, while continual pre-training on original documents has a marginal effect, modifying documents to retain only essential information significantly enhances the insight-learning capabilities of LLMs.

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